ArticleBMC bioinformatics2011
Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 88 citations in OpenAlex.
- Development of Plasma Protein Classification Models for Alzheimer's Disease Using Multiple Machine Learning Approaches.International journal of molecular sciences · 2025Article
- Identification of superior parents with high fiber quality using molecular markers and phenotypes based on a core collection of upland cotton (Molecular breeding : new strategies in plant improvement · 2022Article
- Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops.Frontiers in genetics · 2022Review
- The utility of genomic prediction models in evolutionary genetics.Proceedings. Biological sciences · 2021Article
- Variable Selection Using Nonlocal Priors in High-Dimensional Generalized Linear Models With Application to fMRI Data Analysis.Entropy (Basel, Switzerland) · 2020Article
- A parallelized strategy for epistasis analysis based on Empirical Bayesian Elastic Net models.Bioinformatics (Oxford, England) · 2020Article
- Incorporating Genome Annotation Into Genomic Prediction for Carcass Traits in Chinese Simmental Beef Cattle.Frontiers in genetics · 2020Article
- Climate sensitivity of Cryptomeria japonica in two contrasting environments: Perspectives from QTL mapping.PloS one · 2020Article
- Efficient Estimation of Marker Effects in Plant Breeding.G3 (Bethesda, Md.) · 2019Article
- Identifying the Driving Factors of Black Bloom in Lake Bay through Bayesian LASSO.International journal of environmental research and public health · 2019Article
- MVQTLCIM: composite interval mapping of multivariate traits in a hybrid FBMC bioinformatics · 2017Article
- Epistasis analysis of microRNAs on pathological stages in colon cancer based on an Empirical Bayesian Elastic Net method.BMC genomics · 2017Article
- Genetic dissection of main and epistatic effects of QTL based on augmented triple test cross design.PloS one · 2017Article
- Genomic selection in a commercial winter wheat population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2016Article
- Genetic dissection of heterosis using epistatic association mapping in a partial NCII mating design.Scientific reports · 2015Article
- Potential and limits of whole genome prediction of resistance to Fusarium head blight and Septoria tritici blotch in a vast Central European elite winter wheat population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2015Article
- Modeling Epistasis in Genomic Selection.Genetics · 2015Article
- Composite Interval Mapping Based on Lattice Design for Error Control May Increase Power of Quantitative Trait Locus Detection.PloS one · 2015Article
- Article
- Interacted QTL mapping in partial NCII design provides evidences for breeding by design.PloS one · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Bayesian shrinkage technique has been applied to multiple quantitative trait loci (QTLs) mapping to estimate the genetic effects of QTLs on quantitative traits from a very large set of possible effects including the main and epistatic effects of QTLs. Although the recently developed empirical Bayes (EB) method significantly reduced computation comparing with the fully Bayesian approach, its speed and accuracy are limited by the fact that numerical optimization is required to estimate the variance components in the QTL model.
resultsWe developed a fast empirical Bayesian LASSO (EBLASSO) method for multiple QTL mapping. The fact that the EBLASSO can estimate the variance components in a closed form along with other algorithmic techniques render the EBLASSO method more efficient and accurate. Comparing with the EB method, our simulation study demonstrated that the EBLASSO method could substantially improve the computational speed and detect more QTL effects without increasing the false positive rate. Particularly, the EBLASSO algorithm running on a personal computer could easily handle a linear QTL model with more than 100,000 variables in our simulation study. Real data analysis also demonstrated that the EBLASSO method detected more reasonable effects than the EB method. Comparing with the LASSO, our simulation showed that the current version of the EBLASSO implemented in Matlab had similar speed as the LASSO implemented in Fortran, and that the EBLASSO detected the same number of true effects as the LASSO but a much smaller number of false positive effects.
conclusionsThe EBLASSO method can handle a large number of effects possibly including both the main and epistatic QTL effects, environmental effects and the effects of gene-environment interactions. It will be a very useful tool for multiple QTL mapping.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.